Chemical weathering and gully erosion causing land degradation in a complex river basin of Eastern India: an integrated field, analytical and artificial intelligence approach

2021 ◽  
Author(s):  
Subodh Chandra Pal ◽  
Rabin Chakrabortty ◽  
Alireza Arabameri ◽  
M. Santosh ◽  
Asish Saha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
River Basin ◽  
Land Degradation ◽  
Chemical Weathering ◽  
Gully Erosion ◽  
Eastern India ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

scholarly journals A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2444 ◽  
Author(s):  
Dieu Tien Bui ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ebrahim Omidavr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Kernel Functions ◽  
Gully Erosion ◽  
Support Vector ◽  
Base Function ◽  
Conditioning Factors ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

scholarly journals Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach

2021 ◽  
Vol 413 ◽  
pp. 125358
Author(s):  
Mehrdad Mesgarpour ◽  
Javad Mohebbi Najm Abad ◽  
Rasool Alizadeh ◽  
Somchai Wongwises ◽  
Mohammad Hossein Doranehgard ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Corona Virus ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach
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